Machine Learning Engineer Interview Experience
Interview process
The system design interview was data collection, SFT and RL stages. It went well for me. It was focused on LLM post training techniques. The ML coding was worst for me although I practiced the same question but using PyTorch and doing without PyTorch and without any documentation was challenging for me.
- Recruiter screen
- Technical interview
- Take-home project
Interview rounds
3 questions across 2 roundsTechnical interview
2 questionsQuestion types asked
Specific questions asked
Decode tokens from LLM using top K sampling without using PyTorch
Design knowledge distillation of models. Design LLM post training pipeline.
Take-home project
1 questionQuestion types asked
Specific questions asked
Present a paper from a list provided by them
Interview tips
Brush up numpy knowledge. Every round would be on LLM or related to it. They focus heavily on LLM. The paper presentation is unique in the process for this type of role. Talk to different PhD candidates on how to present a paper well.
Company culture
I liked that the team was focused on post training. You need to work with the frontier model team which is cool and has lot of scope for learning. The remote culture attracted me.
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